為什麼我們轉向使用 Hugging Face Inference Endpoints，或許你也應該試試
Original: Why we’re switching to Hugging Face Inference Endpoints, and maybe you should too
This case study from Mantis NLP details the core reasons behind their decision to migrate their machine learning model deployment workflow…
Mantis NLP 團隊分享了他們將 NLP 模型部署全面轉向 Hugging Face Inference Endpoints 的實戰經驗。相較於傳統自建 AWS SageMaker 或 EC2 基礎設施，Hugging Face 提供極低的維護門檻、靈活的自動縮放（包括縮減至零）以及極具競爭力的價格。這項轉變不僅大幅縮短了產品上線時間，也讓團隊能更專注於模型本身的研發而非繁雜的運維工作。
This case study from Mantis NLP details the core reasons behind their decision to migrate their machine learning model deployment workflow from traditional cloud infrastructure (such as AWS SageMaker or self-managed EC2/ECS) to Hugging Face Inference Endpoints.
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